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Prompting .NET for AI Application Development
Paperback
ISBN13: 9788169645027
Publisher: Orange Education Pvt Ltd
Published: Sep 11 2026
Pages: 280
Weight: 1.07
Height: 0.59 Width: 7.50 Depth: 9.25
Language: English
Don't Just Call AI. Engineer It for Production.
Book Description
Future of .NET Development Is Not AI Integration. It is AI Engineering.
Most .NET developers can call an AI, API. Very few can build AI features that hold up in production. Prompting .NET for AI Application Development treats prompting as engineering - something that belongs inside service boundaries, typed contracts, secure configuration, and observable workflows, not inside a demo that ships once, and breaks quietly.
Starting from prompt design and environment setup, the book builds progressively through .NET architecture patterns, Semantic Kernel orchestration, structured outputs, and integration across ASP.NET Core, Blazor, and .NET MAUI. RAG pipelines, output validation, guardrails, cost controls, and deployment follow - each grounded in practical .NET code and real implementation decisions.
Two complete production scenarios bring it together: An AI-powered e-commerce platform and an intelligent enterprise dashboard. By the end of the book, you will build, harden, and operate .NET AI features that remain reliable when providers fail, inputs change, and applications scale.
What you will learn
● Design reliable prompts for real-world .NET application workflows within clear service boundaries.
● Structure maintainable AI services, typed contracts, and Semantic Kernel orchestration Layers.
● Integrate Semantic Kernel and Azure OpenAI model providers into ASP.NET Core, Blazor, and MAUI.
Table of Contents
1. Prompt-Driven Application Development in .NET
2. Setting up a Production-Ready AI Development Environment in .NET
3. Designing Effective Prompts for Real .NET Workflows
4. .NET Architecture Patterns for AI-Powered Applications
5. Working with Semantic Kernel in Production-Oriented .NET Solutions
6. Building the First End-to-End AI-Powered .NET Application
7. Retrieval-Augmented Generation in Production .NET Applications
8. Prompt Optimization, Reliability, and Control in .NET
9. Building, Deploying, and Scaling an AI-Powered E-Commerce Platform with .NET
10. Building an Intelligent Enterprise Dashboard in .NET
Index
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